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2013 Vol. 35, No. 11
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2013, 35(11): 2541-2546.
doi: 10.3724/SP.J.1146.2013.00188
Abstract:
When the cognitive radios are coexisting with time division duplex systems, it should firstly find out the link directions of the primary systems for the separate design and implementation of the followed spectrum sensing and access. However, in the present studies, this information is assumed as prior information of the cognitive radios, which in fact is not practical in common scenarios. In this paper, a method based on the power levels on both links of TD-LTE is proposed to identify the link directions of subframes and the downlink/uplink configuration in the TD-LTE. This method needs not the cooperation between the primary and secondary systems. Simulations show that the method can meet a high accuracy when a few of frames and SUs are participated in the identification.
When the cognitive radios are coexisting with time division duplex systems, it should firstly find out the link directions of the primary systems for the separate design and implementation of the followed spectrum sensing and access. However, in the present studies, this information is assumed as prior information of the cognitive radios, which in fact is not practical in common scenarios. In this paper, a method based on the power levels on both links of TD-LTE is proposed to identify the link directions of subframes and the downlink/uplink configuration in the TD-LTE. This method needs not the cooperation between the primary and secondary systems. Simulations show that the method can meet a high accuracy when a few of frames and SUs are participated in the identification.
2013, 35(11): 2547-2553.
doi: 10.3724/SP.J.1146.2013.00359
Abstract:
A layered video multicast scheme for broadband wireless networks is proposed. The multicast scheme works by combining layered video coding with Adaptive Modulation and Coding (AMC). The system utility is formulated into a function of user distribution, system resources, and corresponding Modulation and Coding Schemes (MCS). By allocating different resources inter-and intra-multicast groups through nonlinear integer programming and a fast greedy algorithm respectively, the system utility is maximized. The layered video coding structure is then modified according to resource allocation, so that the layered structure is compatible with available system resources and the channel conditions of different users. Simulation results show that this scheme optimizes the layered structure of video coding, takes full advantage of radio resources, and improves the system performance.
A layered video multicast scheme for broadband wireless networks is proposed. The multicast scheme works by combining layered video coding with Adaptive Modulation and Coding (AMC). The system utility is formulated into a function of user distribution, system resources, and corresponding Modulation and Coding Schemes (MCS). By allocating different resources inter-and intra-multicast groups through nonlinear integer programming and a fast greedy algorithm respectively, the system utility is maximized. The layered video coding structure is then modified according to resource allocation, so that the layered structure is compatible with available system resources and the channel conditions of different users. Simulation results show that this scheme optimizes the layered structure of video coding, takes full advantage of radio resources, and improves the system performance.
2013, 35(11): 2554-2559.
doi: 10.3724/SP.J.1146.2012.01362
Abstract:
A scheme for image transmission over wireless channel is proposed. Being rateless, fountain codes could reduce system complexity and no feedback channel is needed. Traditional fountain code has a low decoding efficiency, and the quality of the recovered information is sensitive to noise. Based on systematic Raptor, this method can improve the decoding efficiency because decoding is not even needed in the ideal channel. With the introduction of Unequal Error Protection (UEP) characteristics, this scheme makes the bitrate optimized according to the importance of information, so that a better stability can be achieved at different channel conditions. The experiment results show that, compared with traditional error-correcting codes and Raptor with Equal Error Protection (EEP), this scheme can greatly improve the transmission reliability and achieve better reconstructed image quality over Binary?Erasure Channel (BEC).
A scheme for image transmission over wireless channel is proposed. Being rateless, fountain codes could reduce system complexity and no feedback channel is needed. Traditional fountain code has a low decoding efficiency, and the quality of the recovered information is sensitive to noise. Based on systematic Raptor, this method can improve the decoding efficiency because decoding is not even needed in the ideal channel. With the introduction of Unequal Error Protection (UEP) characteristics, this scheme makes the bitrate optimized according to the importance of information, so that a better stability can be achieved at different channel conditions. The experiment results show that, compared with traditional error-correcting codes and Raptor with Equal Error Protection (EEP), this scheme can greatly improve the transmission reliability and achieve better reconstructed image quality over Binary?Erasure Channel (BEC).
2013, 35(11): 2560-2565.
doi: 10.3724/SP.J.1146.2012.01718
Abstract:
This paper discusses about the trade-off between detection probability, false alarm probability and observation period for the broadband spectrum energy detection. By utilizing the threshold based on the minimum-error-rate criterion, the closed-form expression of the minimum watching time for the detection is derived, which can make the detection satisfy the performance constraints. Besides, the effect of noise uncertainty is also considered in the detection and the expression of watching period with peak noise uncertainty is provided. All conclusions are verified by the numerical simulations.
This paper discusses about the trade-off between detection probability, false alarm probability and observation period for the broadband spectrum energy detection. By utilizing the threshold based on the minimum-error-rate criterion, the closed-form expression of the minimum watching time for the detection is derived, which can make the detection satisfy the performance constraints. Besides, the effect of noise uncertainty is also considered in the detection and the expression of watching period with peak noise uncertainty is provided. All conclusions are verified by the numerical simulations.
2013, 35(11): 2566-2571.
doi: 10.3724/SP.J.1146.2012.01702
Abstract:
Energy consumption is an important consideration in Cooperative Spectrum Sensing (CSS). In order to reduce the data reporting energy and thus prolong the lifetime of Secondary User (SU) with energy constraint in Cognitive Radio Network (CRN), a novel censor-based CSS scheme is proposed. The philosophy of the proposed scheme is to let an SU to report only if the sensing result differs from its previous one. The reporting energy in the proposed scheme is investigated along with comparison, and the spectrum sensing period and energy detection threshold are then optimized subject to sufficient protection to the Primary User (PU). Theoretical analysis and simulation results show that, the proposed CSS scheme reduces the reporting energy significantly without any loss in sensing performance compared with existing studies.
Energy consumption is an important consideration in Cooperative Spectrum Sensing (CSS). In order to reduce the data reporting energy and thus prolong the lifetime of Secondary User (SU) with energy constraint in Cognitive Radio Network (CRN), a novel censor-based CSS scheme is proposed. The philosophy of the proposed scheme is to let an SU to report only if the sensing result differs from its previous one. The reporting energy in the proposed scheme is investigated along with comparison, and the spectrum sensing period and energy detection threshold are then optimized subject to sufficient protection to the Primary User (PU). Theoretical analysis and simulation results show that, the proposed CSS scheme reduces the reporting energy significantly without any loss in sensing performance compared with existing studies.
2013, 35(11): 2572-2578.
doi: 10.3724/SP.J.1146.2012.01728
Abstract:
Considering the Weighted Bit Flipping (WBF) and Modified Weighted Bit Flipping (MWBF) decoding algorithms for Low Density Parity-Check (LDPC) codes, a modified algorithm is proposed in this paper. Based on Average Magnitude (AM), a more simple and efficient method for computing the reliability of the parity checks is introduced to improve the flipping criterion. Simulation results show that the performance of the improved scheme is better than that of WBF and MWBF algorithms about 1.65 dB and 1.36 dB at BER of10-5in the presence of AWGN, while the average number of decoding iterations is reduced by 18.2%~39.91% and 17.54%~34.78%, respectively.
Considering the Weighted Bit Flipping (WBF) and Modified Weighted Bit Flipping (MWBF) decoding algorithms for Low Density Parity-Check (LDPC) codes, a modified algorithm is proposed in this paper. Based on Average Magnitude (AM), a more simple and efficient method for computing the reliability of the parity checks is introduced to improve the flipping criterion. Simulation results show that the performance of the improved scheme is better than that of WBF and MWBF algorithms about 1.65 dB and 1.36 dB at BER of10-5in the presence of AWGN, while the average number of decoding iterations is reduced by 18.2%~39.91% and 17.54%~34.78%, respectively.
2013, 35(11): 2579-2583.
doi: 10.3724/SP.J.1146.2013.00274
Abstract:
In this paper, the distribution of homogeneous distance of (1+u)-constacyclic codes over the ring Rk=Fpm+uFpm++uk-1Fpmof arbitrary lengths is studied. Firstly, the torsion codes of a (1+u)-constacyclic code overRk for a given length are introduced. Then, by using the torsion codes, a bound for the homogeneous distance of (1+u)-constacyclic codes overRk of any length is given. The exact homogeneous distance of some (1+u)-constacyclic codes over Rk is also obtained.
In this paper, the distribution of homogeneous distance of (1+u)-constacyclic codes over the ring Rk=Fpm+uFpm++uk-1Fpmof arbitrary lengths is studied. Firstly, the torsion codes of a (1+u)-constacyclic code overRk for a given length are introduced. Then, by using the torsion codes, a bound for the homogeneous distance of (1+u)-constacyclic codes overRk of any length is given. The exact homogeneous distance of some (1+u)-constacyclic codes over Rk is also obtained.
Capacity of a Class of Semi-deterministic Relay Channel with Orthogonal Components and Channel State
2013, 35(11): 2584-2589.
doi: 10.3724/SP.J.1146.2013.00234
Abstract:
A class of state-dependent relay channel with orthogonal channels from the source to the relay and from the source and the relay to the destination is studied. The two orthogonal channels are corrupted by a common channel state which is known to both the source and the relay non-causally. Based on superposition coding, cooperative GP (Gelfand-Pinsker) coding and PDF (Partial-Decode-and-Forward) relaying, the lower bound on the capacity for this channel is established firstly. Then, for the semi-deterministic relay channel with orthogonal components whose receiver output is a deterministic function of the relay input, one of the source inputs and the channel state, the explicit capacity is characterized exactly.
A class of state-dependent relay channel with orthogonal channels from the source to the relay and from the source and the relay to the destination is studied. The two orthogonal channels are corrupted by a common channel state which is known to both the source and the relay non-causally. Based on superposition coding, cooperative GP (Gelfand-Pinsker) coding and PDF (Partial-Decode-and-Forward) relaying, the lower bound on the capacity for this channel is established firstly. Then, for the semi-deterministic relay channel with orthogonal components whose receiver output is a deterministic function of the relay input, one of the source inputs and the channel state, the explicit capacity is characterized exactly.
2013, 35(11): 2590-2595.
doi: 10.3724/SP.J.1146.2012.01353
Abstract:
In cognitive radio networks, the spectrum handoff performance of secondary users not only relates closely to the communication behavior of primary users, but also related closely to the spectrum sensing accuracy and the spectrum handoff strategies of the secondary users. In this paper, the spectrum handoff schemes that considered with perfect sensing and imperfect sensing are modeled by using the continuous time Markov model, and the affection on the performance of spectrum handoff for different spectrum sensing accuracy is analyzed and compared further. Furthermore, the spectrum handoff strategy based on the channel reservation mechanism is proposed to reduce the forced termination probability of the secondary users effectively in the spectrum handoff process. The numerical results show that the sensing accuracy on the primary signals affects significantly the spectrum handoff performance of secondary users, while the effective spectrum handoff strategy can achieve better performance for spectrum handoff at a lower cost.
In cognitive radio networks, the spectrum handoff performance of secondary users not only relates closely to the communication behavior of primary users, but also related closely to the spectrum sensing accuracy and the spectrum handoff strategies of the secondary users. In this paper, the spectrum handoff schemes that considered with perfect sensing and imperfect sensing are modeled by using the continuous time Markov model, and the affection on the performance of spectrum handoff for different spectrum sensing accuracy is analyzed and compared further. Furthermore, the spectrum handoff strategy based on the channel reservation mechanism is proposed to reduce the forced termination probability of the secondary users effectively in the spectrum handoff process. The numerical results show that the sensing accuracy on the primary signals affects significantly the spectrum handoff performance of secondary users, while the effective spectrum handoff strategy can achieve better performance for spectrum handoff at a lower cost.
2013, 35(11): 2596-2601.
doi: 10.3724/SP.J.1146.2013.00265
Abstract:
To improve the performance and decrease the constraints of fault localization with single distinguish parameter, a multi-parameters link failure localization algorithm is proposed based on compressive sensing and entropy difference. Firstly it makes a fast fault prediction by Bayesian network, then it introduces a parameter named fault coverage and selects probable link failure using compressive sensing, finally defines fault information entropy difference and obtains the root fault based on the parameter. The simulation results show that the predicted fault set can be compressed and the selected probable fault set contains the true fault, meanwhile the proposed algorithm achieves high detection rate and low false positive rate.
To improve the performance and decrease the constraints of fault localization with single distinguish parameter, a multi-parameters link failure localization algorithm is proposed based on compressive sensing and entropy difference. Firstly it makes a fast fault prediction by Bayesian network, then it introduces a parameter named fault coverage and selects probable link failure using compressive sensing, finally defines fault information entropy difference and obtains the root fault based on the parameter. The simulation results show that the predicted fault set can be compressed and the selected probable fault set contains the true fault, meanwhile the proposed algorithm achieves high detection rate and low false positive rate.
2013, 35(11): 2602-2607.
doi: 10.3724/SP.J.1146.2013.00147
Abstract:
Brandsttter et al. (2011) combined the concepts of the two-prime generator and Sidelnikov sequence to define a new sequence called two-prime (p, q) Sidelnikov sequence, and analyzed the balance, the autocorrelation, the correlation measure and the linear complexity profile of the sequence. They showed that this sequence has many nice pseudorandom properties. With the help of the Legendre symbol in number theory and the exponential sums in finite field, this paper investigates the autocorrelation of the two-prime Sidelnikov sequence with d=gcd(p, q)=2. Three theorems are got about the autocorrelation functions. The detailed comparison results show that the bounds O(q1/2) and O(p1/2) on the autocorrelation function in theorem 2 and theorem 3 are tighter than the Brandst?tters bound O((p+q)/2), besides, the bound O((p q) 1/2) in theorem 4 are tighter than the Brandsttters bound O((p+q) /2+(p q) 1/2) when p q or q p.
Brandsttter et al. (2011) combined the concepts of the two-prime generator and Sidelnikov sequence to define a new sequence called two-prime (p, q) Sidelnikov sequence, and analyzed the balance, the autocorrelation, the correlation measure and the linear complexity profile of the sequence. They showed that this sequence has many nice pseudorandom properties. With the help of the Legendre symbol in number theory and the exponential sums in finite field, this paper investigates the autocorrelation of the two-prime Sidelnikov sequence with d=gcd(p, q)=2. Three theorems are got about the autocorrelation functions. The detailed comparison results show that the bounds O(q1/2) and O(p1/2) on the autocorrelation function in theorem 2 and theorem 3 are tighter than the Brandst?tters bound O((p+q)/2), besides, the bound O((p q) 1/2) in theorem 4 are tighter than the Brandsttters bound O((p+q) /2+(p q) 1/2) when p q or q p.
2013, 35(11): 2608-2614.
doi: 10.3724/SP.J.1146.2013.00090
Abstract:
Attack modeling plays an important role in network security analysis and assessment. A Generalized Stochastic Colored Petri Net (GSCPN) model for attack composition is proposed. To each attack, a GSCPN model is constructed to describe the relation of components graphically. Operators to construct attack composition from known ones as blocks are defined formally. The algorithm to construct a composite attack is delivered, and the structural complexity of combination?model is measured also. On this basis, the time cost of vulnerabilities is assessed. The network example validates further the effectiveness of the proposed composition model and calculation method.
Attack modeling plays an important role in network security analysis and assessment. A Generalized Stochastic Colored Petri Net (GSCPN) model for attack composition is proposed. To each attack, a GSCPN model is constructed to describe the relation of components graphically. Operators to construct attack composition from known ones as blocks are defined formally. The algorithm to construct a composite attack is delivered, and the structural complexity of combination?model is measured also. On this basis, the time cost of vulnerabilities is assessed. The network example validates further the effectiveness of the proposed composition model and calculation method.
2013, 35(11): 2615-2622.
doi: 10.3724/SP.J.1146.2012.01735
Abstract:
The existing authentication schemes have the problem of heave calculation and communication overhead in the large-scale delay tolerant network. This paper proposes an authentication scheme for large-scale delay tolerant networks. An efficient hierarchical identity based signature is proposed, which has less overhead compared with the existing schemes and has the aggregate verification property. Based on the proposed signature, an authentication scheme is constructed. A batch authentication is also proposed based on the aggregate verification properties of the signature. The signature and scheme prove to be secure under the h-wDBDHI* and ECDDH assumption. The analysis and simulation show that the authentication overhead and successful rate of this scheme are both better than the existing schemes. The scheme is more suitable for the large-scale delay tolerant networks.
The existing authentication schemes have the problem of heave calculation and communication overhead in the large-scale delay tolerant network. This paper proposes an authentication scheme for large-scale delay tolerant networks. An efficient hierarchical identity based signature is proposed, which has less overhead compared with the existing schemes and has the aggregate verification property. Based on the proposed signature, an authentication scheme is constructed. A batch authentication is also proposed based on the aggregate verification properties of the signature. The signature and scheme prove to be secure under the h-wDBDHI* and ECDDH assumption. The analysis and simulation show that the authentication overhead and successful rate of this scheme are both better than the existing schemes. The scheme is more suitable for the large-scale delay tolerant networks.
2013, 35(11): 2623-2629.
doi: 10.3724/SP.J.1146.2013.00164
Abstract:
To solve the problem of secret sharing between multiparty and multiparty, a new multiparty-to- multiparty quantum secret sharing protocol is proposed based on dense-coding. It performs different local operations on different photons of the Bell state, choosing separately from the random phase shift operations and the combinations of Pauli operations and either Hadamard operation or I operation. This protocol can resist not only the existing attacks such as the dense-coding attack and the entanglement-swapping attack, but also the cheating attack from the agent, which is viewed as verifiability. Meanwhile, the protocol is also high-efficient and can change the sub-secrets and the group of agents dynamically.
To solve the problem of secret sharing between multiparty and multiparty, a new multiparty-to- multiparty quantum secret sharing protocol is proposed based on dense-coding. It performs different local operations on different photons of the Bell state, choosing separately from the random phase shift operations and the combinations of Pauli operations and either Hadamard operation or I operation. This protocol can resist not only the existing attacks such as the dense-coding attack and the entanglement-swapping attack, but also the cheating attack from the agent, which is viewed as verifiability. Meanwhile, the protocol is also high-efficient and can change the sub-secrets and the group of agents dynamically.
2013, 35(11): 2630-2636.
doi: 10.3724/SP.J.1146.2012.01539
Abstract:
The alarms of Intrusion Detective System (IDS) are correlated and analyzed dynamically in a certain interval of time according to the relevant characteristics of real-time alarms. On this basis, a quantitative evaluation approach for real time risk is proposed. Firstly, considering the influence of the strength of security measures and vulnerabilities to attacking results, the attacking success probability algorithm is proposed. Secondly, the attacking threat degree algorithm is proposed, and it can better reflect the difference of threat degree between continuous multi-step attacks and multiple isolated attacks. Finally, the risk situation graph of network nodes is achieved by the weighted sum of each node risk situation value. To verify the validity of the method, a testing platform is built. Experiments show that the method can improve the accuracy of evaluation results, and will help to optimize the safety strategy.
2013, 35(11): 2637-2643.
doi: 10.3724/SP.J.1146.2012.01402
Abstract:
To enhance the survivability of Network Coordinate System (NCS) in un-trusted environment, the physical meaning of anchor nodes spring force in classic model is re-explained, weight vector is taken for anchor nodes reputations instead of their prediction errors. Thus a voter model is proposed for network distance prediction and this model is categorized as a kind of method to solve a l1-loss function minimizing problem. By taking the objective functions non-differentiability into consideration, the incremental sub-gradient descending algorithm is used to minimize this function, and a proportional regulator is used to control the iterative step factor with negative feedback. The experiments show that the proposed model is more accurate than classic model in trusted environment with acceptable computing cost. Furthermore, it can also estimate network distance with moderate accuracy in serious un-trusted environment, and shows a stronger intrusion-tolerance capability than classic model.
To enhance the survivability of Network Coordinate System (NCS) in un-trusted environment, the physical meaning of anchor nodes spring force in classic model is re-explained, weight vector is taken for anchor nodes reputations instead of their prediction errors. Thus a voter model is proposed for network distance prediction and this model is categorized as a kind of method to solve a l1-loss function minimizing problem. By taking the objective functions non-differentiability into consideration, the incremental sub-gradient descending algorithm is used to minimize this function, and a proportional regulator is used to control the iterative step factor with negative feedback. The experiments show that the proposed model is more accurate than classic model in trusted environment with acceptable computing cost. Furthermore, it can also estimate network distance with moderate accuracy in serious un-trusted environment, and shows a stronger intrusion-tolerance capability than classic model.
2013, 35(11): 2644-2649.
doi: 10.3724/SP.J.1146.2013.00254
Abstract:
Network reliability is one of the most important performance in the design of Virtual Network (VN) and gaining more and more attention currently. This paper focuses on the issue of less stringent reliable virtual network mapping. The VN topology remains connected except failed virtual node in the event of single substrate node failure without reserving backup resources. The necessary conditions for mapping VN to be reliable are researched at first, and then the issue is formulated as an Integer Linear Program (ILP) based on it. Finally a novel heuristic algorithm is proposed to solve it. The policy consists of a topology-aware node mapping strategy and a link mapping strategy based on tabu search. Evaluation results show that the proposed heuristic algorithm can obtain the reliable VN mapping with higher substrate long-term average revenue and efficient resource utilization.
Network reliability is one of the most important performance in the design of Virtual Network (VN) and gaining more and more attention currently. This paper focuses on the issue of less stringent reliable virtual network mapping. The VN topology remains connected except failed virtual node in the event of single substrate node failure without reserving backup resources. The necessary conditions for mapping VN to be reliable are researched at first, and then the issue is formulated as an Integer Linear Program (ILP) based on it. Finally a novel heuristic algorithm is proposed to solve it. The policy consists of a topology-aware node mapping strategy and a link mapping strategy based on tabu search. Evaluation results show that the proposed heuristic algorithm can obtain the reliable VN mapping with higher substrate long-term average revenue and efficient resource utilization.
2013, 35(11): 2650-2656.
doi: 10.3724/SP.J.1146.2012.01623
Abstract:
Mobile Ad hoc NETworks (MANET) has inherent characteristic of dynamic topology, distributed collaboration, thus on-demand routing protocols employs multicast mechanism to improve transmission efficiency. However multicast is apt to cause network congestion which makes congestion condition in MANET critical. On the basis of the stochastic characteristics of MANET node and 802.11 DCF basic access mechanisms, the paper proposes an infinite state quasi-birth-and-death model to accurately describe the packet generating, queuing, sending process of individual node. Stationary analysis is performed with matrix-geometric method and the set of equations for packet arrival rate threshold is obtained. The effectiveness of this model is demonstrated by the simulations in OPNET. The contribution of this paper is to provide a mathematical tool for congestion control research.
Mobile Ad hoc NETworks (MANET) has inherent characteristic of dynamic topology, distributed collaboration, thus on-demand routing protocols employs multicast mechanism to improve transmission efficiency. However multicast is apt to cause network congestion which makes congestion condition in MANET critical. On the basis of the stochastic characteristics of MANET node and 802.11 DCF basic access mechanisms, the paper proposes an infinite state quasi-birth-and-death model to accurately describe the packet generating, queuing, sending process of individual node. Stationary analysis is performed with matrix-geometric method and the set of equations for packet arrival rate threshold is obtained. The effectiveness of this model is demonstrated by the simulations in OPNET. The contribution of this paper is to provide a mathematical tool for congestion control research.
2013, 35(11): 2657-2663.
doi: 10.3724/SP.J.1146.2013.00134
Abstract:
To improve the amplitude estimation accuracy of the fluctuating targets in clutter, an adaptive filter based on the cooptimized transmit-receiver with minimum Mean Square Error (MSE) criteria is proposed. The approach is performed in three stages. Firstly, the radar transmits a burst of probing signals to estimate the amplitudes of the out-of-window scatterers, and then the phase-modulated waveform for the next transmission is optimized adaptively based on the estimated information for sidelobe suppression of the large out-of-window scatterers. Finally, adaptive filtering for the echo signals is realized based on the statistical amplitude estimation of the scatterers in each range bin. The proposed method realizes a close-loop feedback system from the receiver and the transmitter. Moreover, it has better estimation accuracy and lower computational complexity in the filtering for the multiple echo signals. The effectiveness of the proposed method is verified by numerical simulation.
To improve the amplitude estimation accuracy of the fluctuating targets in clutter, an adaptive filter based on the cooptimized transmit-receiver with minimum Mean Square Error (MSE) criteria is proposed. The approach is performed in three stages. Firstly, the radar transmits a burst of probing signals to estimate the amplitudes of the out-of-window scatterers, and then the phase-modulated waveform for the next transmission is optimized adaptively based on the estimated information for sidelobe suppression of the large out-of-window scatterers. Finally, adaptive filtering for the echo signals is realized based on the statistical amplitude estimation of the scatterers in each range bin. The proposed method realizes a close-loop feedback system from the receiver and the transmitter. Moreover, it has better estimation accuracy and lower computational complexity in the filtering for the multiple echo signals. The effectiveness of the proposed method is verified by numerical simulation.
2013, 35(11): 2664-2671.
doi: 10.3724/SP.J.1146.2012.01582
Abstract:
In noisy environments, signal reconstruction can be converted into the issue of bound constrained quadratic programming which can be resolved by the regularization programming algorithm, but the reconstruction quality depends heavily on the regularization parameter. Without any apriori knowledge of noise, the Generalized Cross-Validation (GCV) algorithm provides a suitable way for estimation. But in low Signal-to-Noise Ratio (SNR) conditions, it is difficult for GCV to guarantee perfect convergence at the global optimum, which results in the Signal-to-Clutter Ratio (SCR) of the reconstructed image declining and targets missing. For robust reconstruction in low SNR conditions, the enhanced compressive imaging approach based on Multi-Measurement and Dynamic Clustering (MMDC) is proposed in this paper. First, it extracts randomly the original measured data by multiple times. Second, it receives the image series by CS processing. Finally, it implements robust reconstruction by clustering the image series with DC algorithm. Both the simulated and experimental results indicate that MMDC not only improves the reconstruction quality, but also receives effective clutter suppression. Due to the heavy computation of GCV and the insensitivity of MMDC to estimation error, the MMDC based on a simplified GCV algorithm is also proposed in this paper.
In noisy environments, signal reconstruction can be converted into the issue of bound constrained quadratic programming which can be resolved by the regularization programming algorithm, but the reconstruction quality depends heavily on the regularization parameter. Without any apriori knowledge of noise, the Generalized Cross-Validation (GCV) algorithm provides a suitable way for estimation. But in low Signal-to-Noise Ratio (SNR) conditions, it is difficult for GCV to guarantee perfect convergence at the global optimum, which results in the Signal-to-Clutter Ratio (SCR) of the reconstructed image declining and targets missing. For robust reconstruction in low SNR conditions, the enhanced compressive imaging approach based on Multi-Measurement and Dynamic Clustering (MMDC) is proposed in this paper. First, it extracts randomly the original measured data by multiple times. Second, it receives the image series by CS processing. Finally, it implements robust reconstruction by clustering the image series with DC algorithm. Both the simulated and experimental results indicate that MMDC not only improves the reconstruction quality, but also receives effective clutter suppression. Due to the heavy computation of GCV and the insensitivity of MMDC to estimation error, the MMDC based on a simplified GCV algorithm is also proposed in this paper.
2013, 35(11): 2672-2677.
doi: 10.3724/SP.J.1146.2013.00377
Abstract:
Airborne linear array 3D SAR imaging technique is a research focus in the field of radar imaging, and has broad applications in military and civilian. It utilizes a linear antenna array on the wings to obtain an expected resolution in cross track direction. And imaging performance would be influenced by the array configuration directly. As the wings can not be long enough to carry many T/R elements, it is valid to acquire good imaging performance via MIMO radar array. In this paper, array design issue of airborne linear array 3D SAR system is investigated. Based on the spatial convolution principle, the operation law of inversed convolution is summed up. Multiple design methods of linear MIMO array for 3D SAR imaging system are proposed through inversed convolution operation. Then the advantages and disadvantages of each design method are analyzed in the view of imaging properties. The investigation can provide technical support to construction of airborne linear array 3D SAR imaging system.
Airborne linear array 3D SAR imaging technique is a research focus in the field of radar imaging, and has broad applications in military and civilian. It utilizes a linear antenna array on the wings to obtain an expected resolution in cross track direction. And imaging performance would be influenced by the array configuration directly. As the wings can not be long enough to carry many T/R elements, it is valid to acquire good imaging performance via MIMO radar array. In this paper, array design issue of airborne linear array 3D SAR system is investigated. Based on the spatial convolution principle, the operation law of inversed convolution is summed up. Multiple design methods of linear MIMO array for 3D SAR imaging system are proposed through inversed convolution operation. Then the advantages and disadvantages of each design method are analyzed in the view of imaging properties. The investigation can provide technical support to construction of airborne linear array 3D SAR imaging system.
2013, 35(11): 2678-2685.
doi: 10.3724/SP.J.1146.2012.01381
Abstract:
To solve the issue that coherency matrices of the existing Yamaguchi decompositions do not satisfy Nonnegative Eigenvalue Restriction (NER), Yamaguchi decomposition based on hierarchical NER is proposed. It is derived that the NER problem results from the overestimation of scattering powers, and it is pointed out that if the NER problem of remainder coherency matrix is resolved, the NER problems of all coherency matrices are also resolved. Then, the NER methods of the first layer to the fourth layer are proposed orderly based on NonNegative Eigenvalue Decomposition (NNED) to depress the overestimation of scattering powers. For the NER methods, the posterior-layer NER methods need to hierarchically implement the anterior-layer NER methods. The fourth-layer NER method resolves the NER problem of remainder coherency matrix, so the NER problems of all coherency matrices are also resolved. In addition, the fast NNED more efficient than the existing NNED is derived. The experiment result shows that the proposed decomposition can markedly enhance double-bounce scattering power and reduce volume scattering power for urban areas, and enhance surface scattering power for ocean areas.
To solve the issue that coherency matrices of the existing Yamaguchi decompositions do not satisfy Nonnegative Eigenvalue Restriction (NER), Yamaguchi decomposition based on hierarchical NER is proposed. It is derived that the NER problem results from the overestimation of scattering powers, and it is pointed out that if the NER problem of remainder coherency matrix is resolved, the NER problems of all coherency matrices are also resolved. Then, the NER methods of the first layer to the fourth layer are proposed orderly based on NonNegative Eigenvalue Decomposition (NNED) to depress the overestimation of scattering powers. For the NER methods, the posterior-layer NER methods need to hierarchically implement the anterior-layer NER methods. The fourth-layer NER method resolves the NER problem of remainder coherency matrix, so the NER problems of all coherency matrices are also resolved. In addition, the fast NNED more efficient than the existing NNED is derived. The experiment result shows that the proposed decomposition can markedly enhance double-bounce scattering power and reduce volume scattering power for urban areas, and enhance surface scattering power for ocean areas.
2013, 35(11): 2686-2692.
doi: 10.3724/SP.J.1146.2013.00063
Abstract:
In Through-the Wall Imaging (TWI), the clutter, because of its great energy, has great effect on the detection of the targets. For the purpose of clutter suppression, Factor Analysis (FA) method is applied to TWI data. Besides, a new evaluation criterion, image contrast, for selecting the proper factors is introduced. To validate the method experimentally, a set of through-the-wall experiments in an anechoic chamber are designed. The experimental results indicate that the technique based on FA and image contrast can efficiently suppress the clutter without a priori knowledge about targets.
In Through-the Wall Imaging (TWI), the clutter, because of its great energy, has great effect on the detection of the targets. For the purpose of clutter suppression, Factor Analysis (FA) method is applied to TWI data. Besides, a new evaluation criterion, image contrast, for selecting the proper factors is introduced. To validate the method experimentally, a set of through-the-wall experiments in an anechoic chamber are designed. The experimental results indicate that the technique based on FA and image contrast can efficiently suppress the clutter without a priori knowledge about targets.
2013, 35(11): 2693-2699.
doi: 10.3724/SP.J.1146.2013.00077
Abstract:
For the Forward-Looking (FL) radar using a linear Frequency Diverse Array (FDA), this paper ,based on the space-time signal model, particularly studies features of clutter space-time spectrum spread and phase range walk. The feature of clutter spectrum spread represents the clutter spectrum spread in angle-Doppler space. When the stepped-frequency is lower, the number of array elements and pulse are a few, clutter space-time spectrum spread is not evident. The feature of phase range walk just relates to the range, stepped-frequency and pulse repetition frequency; the effect of phase range walk leads to the separateness of range ambiguous clutter spectrum, which benefits the suppression of range ambiguous clutter. The validity of the analysis in this paper is proved with simulation results.
For the Forward-Looking (FL) radar using a linear Frequency Diverse Array (FDA), this paper ,based on the space-time signal model, particularly studies features of clutter space-time spectrum spread and phase range walk. The feature of clutter spectrum spread represents the clutter spectrum spread in angle-Doppler space. When the stepped-frequency is lower, the number of array elements and pulse are a few, clutter space-time spectrum spread is not evident. The feature of phase range walk just relates to the range, stepped-frequency and pulse repetition frequency; the effect of phase range walk leads to the separateness of range ambiguous clutter spectrum, which benefits the suppression of range ambiguous clutter. The validity of the analysis in this paper is proved with simulation results.
2013, 35(11): 2700-2706.
doi: 10.3724/SP.J.1146.2013.00106
Abstract:
To solve the problem of multitarget tracking with the Pulse Doppler (PD) radar in clutters, a novel method based on Probability Hypothesis Density Filter (PHDF) and Data Association (DA) for joint range ambiguity resolving and multitarget tracking with range ambiguity is proposed. The method sets the radar work with a set of Pulse Repetition Frequencies (PRFs) alternately, and obtains the extended measurements set by making multiple hypotheses with the ambiguous measurement generated by the radar. Then, filters with extended measurement set with the PHDF by making full use of the advantages of which that it can eliminate clutters effectively and avoid the association between target and measurement. Finally it implements a track-estimate data association with the outputs of the PHDF and provides target tracks. Simulation results demonstrate that the proposed method can estimate the number of target as well as individual target state, and succeeds in multitarget tracking with range ambiguity in clutters.
To solve the problem of multitarget tracking with the Pulse Doppler (PD) radar in clutters, a novel method based on Probability Hypothesis Density Filter (PHDF) and Data Association (DA) for joint range ambiguity resolving and multitarget tracking with range ambiguity is proposed. The method sets the radar work with a set of Pulse Repetition Frequencies (PRFs) alternately, and obtains the extended measurements set by making multiple hypotheses with the ambiguous measurement generated by the radar. Then, filters with extended measurement set with the PHDF by making full use of the advantages of which that it can eliminate clutters effectively and avoid the association between target and measurement. Finally it implements a track-estimate data association with the outputs of the PHDF and provides target tracks. Simulation results demonstrate that the proposed method can estimate the number of target as well as individual target state, and succeeds in multitarget tracking with range ambiguity in clutters.
2013, 35(11): 2707-2713.
doi: 10.3724/SP.J.1146.2013.00568
Abstract:
The transmit beampattern methods available for the MIMO radar can not be extended into the planer array MIMO radar. With the application of the base-beam and probability selecting methods, an approach to design the transmit beampattern for the planer array MIMO radar is presented in this paper, which is based on the idea that the beampattern of planer array can be synthetised by the beampattern of a horizontal and vertical line array. First, the desired beampattern is accumulated along the azimuth and consequently the 1-D beampattern along the elevation can be formed. The elevation base-beam collection of the vertical line array and the corresponding probability selecting optimization model are formulated. Then, the azimuth base-beam collection of the horizontal line array and the corresponding probability selecting optimization model can also be formulated for the desired azimuth beampattern of a candidate elevation. Finally, the 2-D base-beam collection is synthetised and the corresponding selected probability of the collection elements can be calculated. Consequently, the transmit beampattern and the transmit signal can be well obtained using the convex optimization approaches.
The transmit beampattern methods available for the MIMO radar can not be extended into the planer array MIMO radar. With the application of the base-beam and probability selecting methods, an approach to design the transmit beampattern for the planer array MIMO radar is presented in this paper, which is based on the idea that the beampattern of planer array can be synthetised by the beampattern of a horizontal and vertical line array. First, the desired beampattern is accumulated along the azimuth and consequently the 1-D beampattern along the elevation can be formed. The elevation base-beam collection of the vertical line array and the corresponding probability selecting optimization model are formulated. Then, the azimuth base-beam collection of the horizontal line array and the corresponding probability selecting optimization model can also be formulated for the desired azimuth beampattern of a candidate elevation. Finally, the 2-D base-beam collection is synthetised and the corresponding selected probability of the collection elements can be calculated. Consequently, the transmit beampattern and the transmit signal can be well obtained using the convex optimization approaches.
2013, 35(11): 2714-2720.
doi: 10.3724/SP.J.1146.2013.00045
Abstract:
In this paper, by exploiting the intrinsic sparsity of the moving target in the angle-Doppler domain, a new space time adaptive moving target parameter estimation algorithm is proposed, which uses the technique of sparse recovery to estimate space-time parameter of the moving target. To solve the contradiction between the successful of sparse recovery probability and the higher resolution, a small dictionary is selected to keep the coherence value between every two adjacent columns of the dictionary equal to minimize, and the parameter estimated from the above sparse recovery is regard as a rough result. To obtain a more precise result, a following match filter is applied to the local neighborhood of the obtained rough value. Effectiveness of the new method is verified via simulation examples.
In this paper, by exploiting the intrinsic sparsity of the moving target in the angle-Doppler domain, a new space time adaptive moving target parameter estimation algorithm is proposed, which uses the technique of sparse recovery to estimate space-time parameter of the moving target. To solve the contradiction between the successful of sparse recovery probability and the higher resolution, a small dictionary is selected to keep the coherence value between every two adjacent columns of the dictionary equal to minimize, and the parameter estimated from the above sparse recovery is regard as a rough result. To obtain a more precise result, a following match filter is applied to the local neighborhood of the obtained rough value. Effectiveness of the new method is verified via simulation examples.
2013, 35(11): 2721-2727.
doi: 10.3724/SP.J.1146.2012.01366
Abstract:
The analysis on the statistical characteristics of the surface Normalized Radar Cross Section (NRCS) is the foundation of the Spaceborne Precipitation Radar (SPR) system design, but it is very difficult and costly to measure the global NRCS in long time and the study results about the Ku band global NRCS at small incidence angles are very few in China. In this paper, statistical analysis on the surface NRCS measured is performed by the Tropical Rainfall Measurement Mission (TRMM) satellite in 2011. The results manifest the relations between the statistical characteristics of the surface NRCS (such as mean and standard deviation) and incidence angles. The influence of NRCS statistical characteristics on the SPR system design is analyzed in the parts of system dynamic range, range side-lobes, antenna side-lobes and radar calibration. These analysis results are the foundations of the radar system requirement analysis and radar calibration method selection.
The analysis on the statistical characteristics of the surface Normalized Radar Cross Section (NRCS) is the foundation of the Spaceborne Precipitation Radar (SPR) system design, but it is very difficult and costly to measure the global NRCS in long time and the study results about the Ku band global NRCS at small incidence angles are very few in China. In this paper, statistical analysis on the surface NRCS measured is performed by the Tropical Rainfall Measurement Mission (TRMM) satellite in 2011. The results manifest the relations between the statistical characteristics of the surface NRCS (such as mean and standard deviation) and incidence angles. The influence of NRCS statistical characteristics on the SPR system design is analyzed in the parts of system dynamic range, range side-lobes, antenna side-lobes and radar calibration. These analysis results are the foundations of the radar system requirement analysis and radar calibration method selection.
2013, 35(11): 2728-2732.
doi: 10.3724/SP.J.1146.2013.00251
Abstract:
In order to improve the classification performance of Extreme Learning Machine (ELM) and retain its advantage of the training speed, after a detailed analysis of feature level fusion and decision level fusion, fusion of ELM is proposed. To implement decision level fusion ELM, Probabilistic ELM (PELM) is proposed, which transforms the numeric outputs of ELM to the probabilistic outputs and unifies the outputs in a fixed range. On this basis, an adaptive weighted feature fusion method is introduced, which considers fully the difference accuracy rates of different features without the prior knowledge and subjective definition. Simulation experiments verify the correctness and the validity of the method, thus achieving a higher recognition rate compared to the Support Vector Machine (SVM) and ELM, and a good performace in terms of training time.
In order to improve the classification performance of Extreme Learning Machine (ELM) and retain its advantage of the training speed, after a detailed analysis of feature level fusion and decision level fusion, fusion of ELM is proposed. To implement decision level fusion ELM, Probabilistic ELM (PELM) is proposed, which transforms the numeric outputs of ELM to the probabilistic outputs and unifies the outputs in a fixed range. On this basis, an adaptive weighted feature fusion method is introduced, which considers fully the difference accuracy rates of different features without the prior knowledge and subjective definition. Simulation experiments verify the correctness and the validity of the method, thus achieving a higher recognition rate compared to the Support Vector Machine (SVM) and ELM, and a good performace in terms of training time.
2013, 35(11): 2733-2738.
doi: 10.3724/SP.J.1146.2012.01397
Abstract:
Sparse recovery is a hot topic around the areas of international mathematics and information processing at present, and it is mainly solved by two major strategies including convex relaxation methods and greedy pursuit methods. However, considering the former on efficiency and the latter on ability, they own shortcomings respectively, and neither can recover Gaussian signals with large sparsity level or small measurement level effectively. In this paper, a new sparse recovery algorithm propose is proposed and based on particle swarm optimization combining with the thought of greedy pursuit methods. It is demonstrated by a series of numerical simulations that when compared to other methods, the proposed algorithm could not only achieve better recovery performance, but also runs relatively fast when recovering Gaussian signals with normal sparsity level or normal measurement level.
Sparse recovery is a hot topic around the areas of international mathematics and information processing at present, and it is mainly solved by two major strategies including convex relaxation methods and greedy pursuit methods. However, considering the former on efficiency and the latter on ability, they own shortcomings respectively, and neither can recover Gaussian signals with large sparsity level or small measurement level effectively. In this paper, a new sparse recovery algorithm propose is proposed and based on particle swarm optimization combining with the thought of greedy pursuit methods. It is demonstrated by a series of numerical simulations that when compared to other methods, the proposed algorithm could not only achieve better recovery performance, but also runs relatively fast when recovering Gaussian signals with normal sparsity level or normal measurement level.
2013, 35(11): 2739-2743.
doi: 10.3724/SP.J.1146.2013.00917
Abstract:
To deal with the problem of the dynamic change of the background caused by camera automatic exposure, a camera response model based moving detection algorithm is proposed in the paper. In the initialization stage, through design of energy function based on penalty term and screening of mass data, the response function of the camera is solved. In the on-line detection stage, firstly, by modeling the current frame and background frame to a single Gaussian distribution which contains outliers, Wiener filtering is used to obtain real-time exposure coefficient ratio between current frame and background frame. Then, when the automatic exposure is detected, the background reference frame which is consistent with exposure coefficient of current frame is obtained by response model of the camera, thus wrong detection of motion caused by automatic exposure is eliminated. Experimental results demonstrate that the proposed algorithm stratifies the demands of real-time processing, and it eliminates better the false motion detection caused by automatic exposure compared with the classical method.
To deal with the problem of the dynamic change of the background caused by camera automatic exposure, a camera response model based moving detection algorithm is proposed in the paper. In the initialization stage, through design of energy function based on penalty term and screening of mass data, the response function of the camera is solved. In the on-line detection stage, firstly, by modeling the current frame and background frame to a single Gaussian distribution which contains outliers, Wiener filtering is used to obtain real-time exposure coefficient ratio between current frame and background frame. Then, when the automatic exposure is detected, the background reference frame which is consistent with exposure coefficient of current frame is obtained by response model of the camera, thus wrong detection of motion caused by automatic exposure is eliminated. Experimental results demonstrate that the proposed algorithm stratifies the demands of real-time processing, and it eliminates better the false motion detection caused by automatic exposure compared with the classical method.
2013, 35(11): 2744-2750.
doi: 10.3724/SP.J.1146.2012.01051
Abstract:
A new Neural Network Non-Uniformity Correction (PM-NN-NUC) algorithm is proposed for InfraRed Focal Plane Array (IRFPA) based on Perona Malik (PM) diffusion for the situation of degradation model both containing fix pattern noise and Gaussian noise in infrared image. A minimize model is established concerning Non-Uniformity Correction (NUC). It can be seen that PM-NN-NUC uses a similarity in the filtering process on Neural Network Non-Uniformity Correction and PM diffusion, and not only generates the expectation directly but also calculates the iterative step. Correction coefficient reacts on PM diffusion process and combines with PM diffusion and Neural Network Non-Uniformity Correction uniformly. The results of real infrared thermal image show that the proposed algorithm eliminates the fixed pattern noise effectively, but also has excellent performance for the image degraded with fade-out.
A new Neural Network Non-Uniformity Correction (PM-NN-NUC) algorithm is proposed for InfraRed Focal Plane Array (IRFPA) based on Perona Malik (PM) diffusion for the situation of degradation model both containing fix pattern noise and Gaussian noise in infrared image. A minimize model is established concerning Non-Uniformity Correction (NUC). It can be seen that PM-NN-NUC uses a similarity in the filtering process on Neural Network Non-Uniformity Correction and PM diffusion, and not only generates the expectation directly but also calculates the iterative step. Correction coefficient reacts on PM diffusion process and combines with PM diffusion and Neural Network Non-Uniformity Correction uniformly. The results of real infrared thermal image show that the proposed algorithm eliminates the fixed pattern noise effectively, but also has excellent performance for the image degraded with fade-out.
2013, 35(11): 2751-2757.
doi: 10.3724/SP.J.1146.2013.00347
Abstract:
Keypoint matching is an important task of computer vision and the major problem is to find a fast and robust keypoints algorithm. This paper presents a binary descriptor matching algorithm based on hierarchical learning method. The descriptor learning process is divided into two levels of coarse and fine, which combines the advantages of the fixed-point sampling mode and random sampling mode, and the process enhances the performance of learning. Meanwhile, a more reasonable point-pair identification model is built and applied into the keypoint matching algorithm which improves the matching precision. Experimental results demonstrate that the proposed algorithm outperforms the classical methods with lower computation time.
Keypoint matching is an important task of computer vision and the major problem is to find a fast and robust keypoints algorithm. This paper presents a binary descriptor matching algorithm based on hierarchical learning method. The descriptor learning process is divided into two levels of coarse and fine, which combines the advantages of the fixed-point sampling mode and random sampling mode, and the process enhances the performance of learning. Meanwhile, a more reasonable point-pair identification model is built and applied into the keypoint matching algorithm which improves the matching precision. Experimental results demonstrate that the proposed algorithm outperforms the classical methods with lower computation time.
2013, 35(11): 2758-2763.
doi: 10.3724/SP.J.1146.2012.01334
Abstract:
In order to solve the problem of performance degradation due to the imprecise knowledge of the array steering vector and inaccurate estimation of the sample covariance matrix. A new approach based on beamspace steering vector estimation for robust adaptive beamforming is presented in this paper. Firstly, by using the complementary set of the spatial sector in which the actual steering vector lies, beamspace transformation matrix can be constructed to ensure that the signal of interest is removed from the sampling covariance matrix. Then a method for beamspace steering vector estimation is derived, and mathematically expressed as the nonconvex Quadratically Constrained Quadratic Programs (QCQP) problem with one non-convex quadratic equality constraint, which can be successfully solved by using SemiDefinite Relaxation (SDR) techniques. Simulation results show the effectiveness of the proposed algorithm.
In order to solve the problem of performance degradation due to the imprecise knowledge of the array steering vector and inaccurate estimation of the sample covariance matrix. A new approach based on beamspace steering vector estimation for robust adaptive beamforming is presented in this paper. Firstly, by using the complementary set of the spatial sector in which the actual steering vector lies, beamspace transformation matrix can be constructed to ensure that the signal of interest is removed from the sampling covariance matrix. Then a method for beamspace steering vector estimation is derived, and mathematically expressed as the nonconvex Quadratically Constrained Quadratic Programs (QCQP) problem with one non-convex quadratic equality constraint, which can be successfully solved by using SemiDefinite Relaxation (SDR) techniques. Simulation results show the effectiveness of the proposed algorithm.
2013, 35(11): 2764-2770.
doi: 10.3724/SP.J.1146.2013.00087
Abstract:
Adaptive beamformers are sensitive to model mismatch and moving interference, especially when the desired signal is present in training snapshots, even a small error can lead to a serious degradation in performance. A robust adaptive beamforming algorithm with null widening is proposed, which is performed by reconstructing and optimizing the interference-plus-noise covariance matrix, and estimating the true steering vector. The sidelobe can be optimized by adjusting the beam width of the mainlobe. Simulation results demonstrate the correctness and effectiveness of the proposed algorithm.
Adaptive beamformers are sensitive to model mismatch and moving interference, especially when the desired signal is present in training snapshots, even a small error can lead to a serious degradation in performance. A robust adaptive beamforming algorithm with null widening is proposed, which is performed by reconstructing and optimizing the interference-plus-noise covariance matrix, and estimating the true steering vector. The sidelobe can be optimized by adjusting the beam width of the mainlobe. Simulation results demonstrate the correctness and effectiveness of the proposed algorithm.
2013, 35(11): 2771-2777.
doi: 10.3724/SP.J.1146.2012.01554
Abstract:
This paper proposes a category of preprocessing functions and the conditions they should meet as to the problems that the type of traditional preprocessing functions is limited and the analysis for the performance improvement caused by the functions is inadequate. Through mathematical reasoning the conclusion is drawn that the variance of the received signals fractional lower-order covariance decreases and is unbiased by passing them through the preprocessing functions, improving the detection precision of the peak of the fractional lower-order covariance, that results in the improvement of precision of time delay estimation. Finally, two preprocessing functions are proposed. The simulation results indicat that the proposed method is effective and can be used under Gaussian noises.
This paper proposes a category of preprocessing functions and the conditions they should meet as to the problems that the type of traditional preprocessing functions is limited and the analysis for the performance improvement caused by the functions is inadequate. Through mathematical reasoning the conclusion is drawn that the variance of the received signals fractional lower-order covariance decreases and is unbiased by passing them through the preprocessing functions, improving the detection precision of the peak of the fractional lower-order covariance, that results in the improvement of precision of time delay estimation. Finally, two preprocessing functions are proposed. The simulation results indicat that the proposed method is effective and can be used under Gaussian noises.
2013, 35(11): 2778-2783.
doi: 10.3724/SP.J.1146.2013.00101
Abstract:
This paper studies the quantum theory and the principle of Quantum Neural Network (QNN). Model of Quantum Forward Counter Propagation Neural Network (QFCPNN) and Recursive?Weighted Least Squares Quantum Forward Counter Propagation Algorithm (RWLS_QFCPA) are analyzed. Definition and knowledge set of QFCPNN is proposed. Adaptive Quantum Forward Counter Propagation Algorithm (AQFCPA) is proposed and its convergence is proved. Full account of overall situations of learning rates before current learning, this algorithm improves network convergence by adaptively changing the learning rate and controls timely changing learning rate. This new algorithm effectively overcomes some defects including network oscillations divergence due to high learning rate and reducing network convergence speed due to low learning rate. The simulation results indicate that AQFCPA has less number of iterations of network training and higher classification accuracy relative to RWLS_QFCPA.
This paper studies the quantum theory and the principle of Quantum Neural Network (QNN). Model of Quantum Forward Counter Propagation Neural Network (QFCPNN) and Recursive?Weighted Least Squares Quantum Forward Counter Propagation Algorithm (RWLS_QFCPA) are analyzed. Definition and knowledge set of QFCPNN is proposed. Adaptive Quantum Forward Counter Propagation Algorithm (AQFCPA) is proposed and its convergence is proved. Full account of overall situations of learning rates before current learning, this algorithm improves network convergence by adaptively changing the learning rate and controls timely changing learning rate. This new algorithm effectively overcomes some defects including network oscillations divergence due to high learning rate and reducing network convergence speed due to low learning rate. The simulation results indicate that AQFCPA has less number of iterations of network training and higher classification accuracy relative to RWLS_QFCPA.
2013, 35(11): 2784-2789.
doi: 10.3724/SP.J.1146.2013.00242
Abstract:
From the perspective of routing architecture, a leakage reduction method of switch matrixes in FPGA is proposed. Based on the conclusion of state-dependent leakage, the lowest leakage current of switch matrixes in FPGA is equivalently computed in a small size of matrix cell using the transition property of SWitch Box (SWB). Because the presented algorithm could research the lowest leakage state in finite SWB output state combinations, rather than confirming SWB output state by level-restoring circuit, the algorithm is used for efficient reduction of leakage in switch matrixes and is compatible with the optimization of leakage at the circuit-level.
From the perspective of routing architecture, a leakage reduction method of switch matrixes in FPGA is proposed. Based on the conclusion of state-dependent leakage, the lowest leakage current of switch matrixes in FPGA is equivalently computed in a small size of matrix cell using the transition property of SWitch Box (SWB). Because the presented algorithm could research the lowest leakage state in finite SWB output state combinations, rather than confirming SWB output state by level-restoring circuit, the algorithm is used for efficient reduction of leakage in switch matrixes and is compatible with the optimization of leakage at the circuit-level.
2013, 35(11): 2790-2794.
doi: 10.3724/SP.J.1146.2013.00223
Abstract:
For any given signal, determining the existence of its Fourier transform has remained an unsolved problem. This raises many concerns in the teaching of signal analysis courses, and also in many engineering applications. This paper presents an analysis of the problem. By comparing the characteristics of the ramp signal,s Fourier transform with that of the Fourier transforms of common energy signals and power signals, the author points out some problems regarding the Fourier transform of the ramp signal. This analysis shows that ramp signal does not satisfy the Fourier integral theorem, thus invalidating the Fourier transformation of ramp signal. Finally, the author further proves this assertion through simulation analysis, and summarizes the general rules of some common signal,s Fourier transform.
For any given signal, determining the existence of its Fourier transform has remained an unsolved problem. This raises many concerns in the teaching of signal analysis courses, and also in many engineering applications. This paper presents an analysis of the problem. By comparing the characteristics of the ramp signal,s Fourier transform with that of the Fourier transforms of common energy signals and power signals, the author points out some problems regarding the Fourier transform of the ramp signal. This analysis shows that ramp signal does not satisfy the Fourier integral theorem, thus invalidating the Fourier transformation of ramp signal. Finally, the author further proves this assertion through simulation analysis, and summarizes the general rules of some common signal,s Fourier transform.
